Relevant bibliographies by topics / IMAGE PATCHING

Academic literature on the topic 'IMAGE PATCHING'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "IMAGE PATCHING"

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Tsai, Yi-Chang (James), Yi-Ching Wu, and Geoffrey Price. "A Cost-Effective and Objective Full-Depth Patching Identification Method using 3D Sensing Technology with Automated Crack Detection and Classification." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (November 29, 2018): 50–58. http://dx.doi.org/10.1177/0361198118798474.

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Full-depth patching is one of the commonly used asphalt pavement maintenance and rehabilitation methods in which deteriorated base and surface layers are repaired to restore strength and improve ride quality. During resurfacing projects, areas requiring full-depth patching are identified and quantified as construction priorities because of the high costs associated with the labor and materials for the procedure. Currently, the manual surveys conducted to identify these locations are time-consuming and labor-intensive. Thus, large projects often cannot easily quantify the full-depth patching need because of the significant labor that would be required. This paper proposes a method that uses emerging 3D laser technology to identify the full-depth patching need by processing and analyzing the pavement distresses automatically extracted from 3D laser images. The proposed method consists of five steps: (1) 3D data acquisition, calibration, and validation, (2) crack detection, (3) crack classification, (4) rutting detection and measurement, and (5) determination of image-based patching need using the established decision tree. A case study of one mile of 3D pavement images, collected from US 80/S.R. 26, was conducted to demonstrate the use and feasibility of the proposed method. Results show the proposed method is capable of correctly classifying 95.4% of the images that show pavements requiring patching and 84.2% of the images showing pavements not requiring patching for a combined accuracy of 94.1%. The method shows promise for identifying patch locations in a cost-effective manner and will save money and time for transportation agencies.
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Zhong, Haidong, Xianyi Chen, and Qinglong Tian. "An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching." Information 10, no. 1 (January 5, 2019): 17. http://dx.doi.org/10.3390/info10010017.

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Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method for reducing the auxiliary information is proposed. Firstly, we divide secret and target images into non-overlapping blocks, and classify these blocks into K classes by using the K-means clustering method. Secondly, we match blocks in the last (K-T)-classes using the traditional RIT method for a threshold T, in which the secret and target blocks are paired with the same compound index. Thirdly, the accessorial information (AI) produced by the matching can be represented as a secret segment, and the secret segment can be hided by patching blocks in the first T-classes. Experimental results show that the proposed strategy can reduce the AI and improve the stego-image quality effectively.
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Gong, Yan. "Panoramic Image Patching Algorithm Based on Global Optimization." Journal of Information and Computational Science 12, no. 14 (September 20, 2015): 5523–30. http://dx.doi.org/10.12733/jics20150019.

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Celaya-Padilla, Jose M., Carlos E. Galvan T, J. Ruben Delgado C, Issac Galvan-Tejada, and Ernesto Ivan Sandoval. "Multi-seed texture synthesis to fast image patching." Procedia Engineering 35 (2012): 210–16. http://dx.doi.org/10.1016/j.proeng.2012.04.182.

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Totsuka, Satoru, Tomoya Handa, Hitoshi Ishikawa, and Nobuyuki Shoji. "Improvement of Adherence with Occlu-Pad Therapy for Pediatric Patients with Amblyopia." BioMed Research International 2018 (November 22, 2018): 1–5. http://dx.doi.org/10.1155/2018/2394562.

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We aimed to examine visual acuity improvement effect and adherence in amblyopia training using tablet type vision training equipment (Occlu-pad). The subjects were 138 patients with amblyopia (average age of 5.5 ± 1.6 years old); their amblyopic visual acuity at the start of training was logMAR 0.15 to 1.3. Occlu-pad is a device that processes images such that amblyopic eyes can only view the image as it passes through polarized glasses; this is achieved by peeling off the polarizing film layer in the liquid crystal display of an iPad (Apple). Amblyopia training comprised either the instructional training with Occlu-pad or the eye patch (Patching) as a family training, after wearing perfectly corrected glasses. Visual acuity improvement following amblyopia training by Occlu-pad and Patching was significantly different after 6 months in patients with anisometropic amblyopia (p <0.05). In patients with strabismic amblyopia, a significant difference between training methods was observed after 9 months (p <0.05). Use of the Occlu-pad resulted in better adherence for patients with either anisometropic amblyopia or strabismic amblyopia; a significant difference in adherence was observed after 3 months, compared with Patching (p <0.05). Amblyopia training with Occlu-pad supports greater visual acuity improvement and adherence than Patching.
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Dan, Han-Cheng, Hao-Fan Zeng, Zhi-Heng Zhu, Ge-Wen Bai, and Wei Cao. "Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network." Sustainability 14, no. 2 (January 12, 2022): 861. http://dx.doi.org/10.3390/su14020861.

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Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural network, using active learning combined with reverse and correction labeling. According to the calculation results in this paper, the sample size required by the interactive labeling is about half of the traditional labeling method for the same recognition precision. Meanwhile, the accuracy of interactive labeling method based on the mean intersection over union (mean_IOU) index is 6% higher than that of the traditional method using the same sample size and training epochs. In addition, the accuracy analysis of the noise and boundary of the prediction results shows that this method eliminates 92% of the noise in the predictions (the proportion of noise is reduced from 13.85% to 1.06%), and the image definition is improved by 14.1% in terms of the boundary gray area ratio. The interactive labeling is considered as a significantly valuable approach, as it reduces the sample size in each epoch of active learning, greatly alleviates the demand for manpower, and improves learning efficiency and accuracy.
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TOKUDA, Kenichi, Tetsuya KINUGASA, Ryota HAYASHI, Takafumi HAJI, and Hisanori AMANO. "Shredded Image Patching of Inner Crawler Cameras for Disaster Robot." Proceedings of Mechanical Engineering Congress, Japan 2016 (2016): G1500504. http://dx.doi.org/10.1299/jsmemecj.2016.g1500504.

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Takahashi, Ryo, Takashi Matsubara, and Kuniaki Uehara. "Data Augmentation Using Random Image Cropping and Patching for Deep CNNs." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (September 2020): 2917–31. http://dx.doi.org/10.1109/tcsvt.2019.2935128.

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Maeda, Keisuke, Saya Takada, Tomoki Haruyama, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching." Sensors 22, no. 22 (November 18, 2022): 8932. http://dx.doi.org/10.3390/s22228932.

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Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress detection model using the expanded training data. The new image generated based on SICAP does not change the pixel values of the original image. Thus, there is little loss of information, and the generated images are effective in constructing a robust model for various subway tunnel lines. We conducted experiments with some comparative methods. The experimental results show that the detection performance can be improved by our data augmentation.
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WANG, DACHENG, and SARGUR N. SRIHARI. "ANALYSIS OF FORM IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (October 1994): 1031–52. http://dx.doi.org/10.1142/s0218001494000528.

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Automatic analysis of images of forms is a problem of both practical and theoretical interest; due to its importance in office automation, and due to the conceptual challenges posed for document image analysis, respectively. We describe an approach to the extraction of text, both typed and handwritten, from scanned and digitized images of filled-out forms. In decomposing a filled-out form into three basic components of boxes, line segments and the remainder (handwritten and typed characters, words, and logos), the method does not use a priori knowledge of form structure. The input binary image is first segmented into small and large connected components. Complex boxes are decomposed into elementary regions using an approach based on key-point analysis. Handwritten and machine-printed text that touches or overlaps guide lines and boxes are separated by removing lines. Characters broken by line removal are rejoined using a character patching method. Experimental results with filled-out forms, from several different domains (insurance, banking, tax, retail and postal) are given.
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Dissertations / Theses on the topic "IMAGE PATCHING"

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Hintze, Ryan Sears. "Shadow Patching: Exemplar-Based Shadow Removal." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6664.

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Shadow removal is an important problem for both artists and algorithms. Previous methods handle some shadows well but, because they rely on the shadowed data, perform poorly in cases with severe degradation. Image-completion algorithms can completely replace severely degraded shadowed regions, and perform well with smaller-scale textures, but often fail to reproduce larger-scale macrostructure that may still be visible in the shadowed region. This paper provides a general framework that leverages degraded (e.g., shadowed) data to guide the image completion process by extending the objective function commonly used in current state-of-the-art image completion energy-minimization methods. This approach achieves realistic shadow removal even in cases of severe degradation and could be extended to other types of localized degradation.
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Lin, Yung-Hsiang, and 林詠翔. "Patching Image Using Genetic Algorithms." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/63269037506952820457.

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碩士
淡江大學
資訊管理學系碩士班
96
In many areas of computer applications, such as MultiMedia Systems and Image Information Systems , image data is always huge and complicated. However, it includes some information which is not important. In fact, we are only interested in the information of importance. In this paper, we use the genetic algorithm to find a set of patches to represent and index the important part of an image. By using the set of patches to represent the image, the storage required can be reduced and the efficiency of processing can be increased.
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JieLin and 林. 倢. "Automated Detection and Patching of Erroneous Areas in Shadow Moiré Phase-Shifting Images and System Calibration under Heating Condition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/yrwy75.

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碩士
國立成功大學
機械工程學系碩博士班
101
Grinding wheel surface has artificial diamonds and the printed circuit board contains many reflective spots and cavities on the surface. When using the shadow moiré system to measure the 3D surface profile of grinding wheel and printed circuit board, these diamonds, reflective spots and cavities lead to wrong gray level on phase-shifting patterns and result in error. When measuring specimen by shadow moiré system under heating condition, the system will produce unpredictable changes because of thermal expansion and cause measurement error. In this paper, using edge detection for averaged image of the four fringe patterns and setting thresholds to distinguish erroneous areas in phase-shifting images. The structure element size of proper-opening and proper-closing to automate detection and patching of erroneous areas in phase-shifting Images is decided by the maximum erroneous area of bounding rectangle width and height. The error of unwrapping phase could be eliminated by using 3x3 median filter in phase-shifting patterns or wrapping phase and make it successfully. To get error of measured value and standard value in different temperature, calibration plate is measured. In this paper, using one order least squares method and cubic spline method to fit error of measured value and standard value to establish program of shadow moiré system calibration under heating condition. By testing, the error using cubic spline is smaller than using one order least squares.
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Book chapters on the topic "IMAGE PATCHING"

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Singh, Amanpreet, and Sharan Agrawal. "CanvasGAN: A Simple Baseline for Text to Image Generation by Incrementally Patching a Canvas." In Advances in Intelligent Systems and Computing, 86–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_7.

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Britton, Ren Loren, and Isabel Paehr. "What is it, I do(n't) not know? I (we) do not know (but will feel) the name." In Doing Research - Wissenschaftspraktiken zwischen Positionierung und Suchanfrage, 288–95. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839456323-035.

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In this article Britton and Paehr play with language around concrete, which makes-firm in language ("Please make this more concrete!") and hardens the grounds that feet, canes and wheels touch every day. Through three patched, sprouted and decomposed collages of concrete material experiments, attempt to unsettle concrete's supposed firmness. N. N. is that which has not been named. In these images N. N. is not concretized and remains open. We are finding ways of making other worlds possible that resist the foreclosing of access or possibilities. The work of these authors germinates with the crip technoscience manifesto from Aimi Hamraie and Kelly Fritsch where access is defined as both related to attack and contact (2019). Instead of the 'integration' of disabled body-minds into normative space, Hamraie and Fritsch uphold the ways in which disabled makers hack or make otherwise trans*feminist presents. Hacking and playing with concrete, we find moments of formlessness through patching, decomposing and sprouting. The authors discover access potentials within something that has been figured as hard, and yet is ontologically, metaphorically and physically not stable. By keeping concrete "open" by adding more water or other materials to interrupt the "curing", and by churning/stirring this mixture, sprouting, decomposing and patchworking operate as practices that resist hardening, firming, closing and foreclosing. N.N. (nomen nomiandum) holds things open and resists formation because, I may not know, but together we can access it.
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Conference papers on the topic "IMAGE PATCHING"

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Johnston, R. A., and N. B. Price. "RBF patching of laser scanner data." In 2008 23rd International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE, 2008. http://dx.doi.org/10.1109/ivcnz.2008.4762077.

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Hintze, Ryan, and Bryan Morse. "Shadow Patching: Guided Image Completion for Shadow Removal." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00217.

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Parihar, Anil Singh. "Object tracking based hybrid context and image patching models." In 2017 International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2017. http://dx.doi.org/10.1109/icici.2017.8365392.

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Zhang, Huimin, Hanyu Hong, Xiuhua Zhang, and Jiuyang Yu. "Extraction algorithm of pavement breakage information based on patching feature." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Bruce Hirsch, Zhiguo Cao, and Hanqing Lu. SPIE, 2009. http://dx.doi.org/10.1117/12.833150.

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Hu, Xin, Xinyu Wang, Yanfei Zhong, Ji Zhao, Chang Luo, and Lifei Wei. "SPNet: A Spectral Patching Network for End-To-End Hyperspectral Image Classification." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8897843.

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Feng, Yong, Jian Liu, Hongyan Xu, and Ye Yuan. "Hole patching strategy with the least mobile nodes in wireless sensor network of agriculture." In Third International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), edited by Honghua Tan. SPIE, 2013. http://dx.doi.org/10.1117/12.2020128.

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Yang, Shaopeng, Weiyu Guo, and Yuheng Ren. "CrowdFormer: An Overlap Patching Vision Transformer for Top-Down Crowd Counting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/215.

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Crowd counting methods typically predict a density map as an intermediate representation of counting, and achieve good performance. However, due to the perspective phenomenon, there is a scale variation in real scenes, which causes the density map-based methods suffer from a severe scene generalization problem because only a limited number of scales are fitted in density map prediction and generation. To address this issue, we propose a novel vision transformer network, i.e., CrowdFormer, and a density kernels fusion framework for more accurate density map estimation and generation, respectively. Thereafter, we incorporate these two innovations into an adaptive learning system, which can take both the annotation dot map and original image as input, and jointly learns the density map estimator and generator within an end-to-end framework. The experimental results demonstrate that the proposed model achieves the state-of-the-art in the terms of MAE and MSE (e.g., it achieved a MAE of 67.1 and MSE of 301.6 on NWPU-Crowd dataset.), and confirm the effectiveness of the proposed two designs. The code is https://github.com/special-yang/Top_Down-CrowdCounting.
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TOKUDA, K., J. NAGAISHI, T. KINUGASA, T. HAJI, and H. AMANO. "SHREDDED IMAGE PATCHING BY USING PAST FRAMES OF INNER CRAWLER CAMERAS FOR FLEXIBLE MONO-TREAD MOBILE TRACK." In CLAWAR 2017: 20th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813231047_0063.

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Mermet, Alexis, Asanobu Kitamoto, Chikahiko Suzuki, and Akira Takagishi. "Face Detection on Pre-modern Japanese Artworks using R-CNN and Image Patching for Semi-Automatic Annotation." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3423323.3423412.

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Huang, Po-Whei, Cheng-I. Chen, Ping Chen, Phen-Lan Lin, and Li-Pin Hsu. "PET and MRI brain image fusion using wavelet transform with structural information adjustment and spectral information patching." In 2014 International Symposium on Bioelectronics and Bioinformatics (ISBB). IEEE, 2014. http://dx.doi.org/10.1109/isbb.2014.6820901.

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Reports on the topic "IMAGE PATCHING"

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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